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In this comprehensive course, learners will explore machine learning, data science, and generative AI using Python. You will gain a solid understanding of machine learning principles, including supervised and unsupervised learning techniques, and apply algorithms such as linear regression, decision trees, and support vector machines.
The course delves into advanced AI concepts like GANs, Variational Autoencoders, and Transformer architecture, which powers models like GPT. You鈥檒l develop, train, and fine-tune models to solve real-world problems such as recommendation systems and sentiment analysis.
By the end of the course, you鈥檒l have hands-on experience with tools like TensorFlow, Keras, and Apache Spark to handle large-scale data challenges. You鈥檒l also learn to deploy models in real-time systems while considering AI ethics.
This course is ideal for those eager to explore data science, machine learning, and AI. Whether you're a data scientist, developer, or enthusiast, you'll gain the practical skills needed to thrive in the rapidly evolving AI field.
Applied Learning Project
In this course, you鈥檒l apply the skills learned to real-world projects like predicting car prices, building recommender systems, and deploying machine learning models on large datasets with Apache Spark. These projects offer practical hands-on experience in solving industry-related challenges.
